Introduction

This report aims to analyze the emotional statuses shown in different philosophers’ texts contained in our Philosophy Data Project dataset. In the dataset, we have 360808 quotes, 13 different schools of philosophy (plato, aristotle, empiricism, rationalism, analytic, continental, phenomenology, german_idealism, communism, capitalism, stoicism, nietzche, feminism), and 36 philosophers (Plato, Aristotle, Locke, Hume, Berkeley, Spinoza, Leibniz, Descartes, Malesbranche, Russell, Moore, Wittgenstein, Lewis, Quine, Popper, Kripke, Foucault, Derrida, Deleuze, Merleau-Ponty, Husserl, Heidegger, Kant, Fichte, Hegel, Marx, Lenin, Smith, Ricardo, Keynes, Epictetus, Marcus Aurelius, Nietzsche, Wollstonecraft, Beauvoir, Davis )

Data Loading & Packages Download

The quotes are subset into words as the emotional analysis is done at word level. The emotional analysis is designed to answer the scientific question “What are those philosophers’ emotional status? (positive vs negative) & how are their emotional status categorized ? & in how many number of clusters can we group those different philosophers based on their emotional status?”

##   author school                  title     word
## 1  Plato  plato Plato - Complete Works     what
## 2  Plato  plato Plato - Complete Works      new
## 3  Plato  plato Plato - Complete Works socrates
## 4  Plato  plato Plato - Complete Works       to
## 5  Plato  plato Plato - Complete Works     make
## 6  Plato  plato Plato - Complete Works      you

Data Analysis : Positive vs Negative

In the binary emotional analysis (positive vs negative) below, at both philosopher level and school level, we see that except a few cases, most of the philosophers and schools exhibit evenly balanced emotional (positive vs negative ) words ratio around 0.5.

The exceptional cases include Foucault(Continental), Fichte(german_idealism), Capitalism, Plato.

bing <- get_sentiments("bing")
nrc <- get_sentiments("nrc")

tidy_bing <- df_words %>% inner_join(bing)

# Philosopher Level 
tidy_bing %>% 
  group_by(author) %>% 
  count(sentiment) %>%
  ungroup() %>%
  ggplot(aes(n,author, fill = sentiment))+
  geom_col(position = "fill") +
  geom_text(aes(label = n), position = position_fill(0.5),  color = "white")+
  theme_dark()+
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold")
    )+
  scale_fill_manual(values = c("#EA181E", "#00B4E8"))+
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10))+
  
  labs(y = NULL,  x = "ratio", fill = NULL, title = "Negative-Positive Ratio at Philosopher Level")

# School Level
tidy_bing %>% 
  group_by(school) %>% 
  count(sentiment) %>%
  ungroup() %>%
  ggplot(aes(n,school, fill = sentiment))+
  geom_col(position = "fill") +
  geom_text(aes(label = n), position = position_fill(0.5),  color = "white")+
  theme_dark()+
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold")
    )+
  scale_fill_manual(values = c("#EA181E", "#00B4E8"))+
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10))+
  
  labs(y = NULL,  x = "ratio", fill = NULL, title = "Negative-Positive Ratio at School Level")

Data Analysis : Sentiment Analysis NRC

In the non binary ( more emotional catergories : anger, anticipation, disgust, fear, joy, negative, positive, sadness, surprise, trust ), at both philosopher level and school level, we see that ‘positive’ and ‘trust’ words account for majority of word counts for most philosophers and schools whereas ‘anger’, ‘disgust’, and ‘fear’ words account for the small portion. IEvery philosopher shares similarity in overall ratio of each emotion.

# Philosopher Level 
tidy_nrc <- df_words %>% inner_join(nrc)
tidy_nrc %>% 
  group_by(author) %>%
  count(sentiment) %>%
  ungroup() %>%
  ggplot(aes(n, author, fill = sentiment))+ 
  geom_col(position = "fill")

# School Level
tidy_nrc <- df_words %>% inner_join(nrc)
tidy_nrc %>% 
  group_by(school) %>%
  count(sentiment) %>%
  ungroup() %>%
  ggplot(aes(n, school, fill = sentiment))+ 
  geom_col(position = "fill")

Emotional Words Analysis for each school

In the emotional word counts analysis below, the each school’s 100 most frequently used emotional words are plotted in the size proportional to their frequency. As can be seen in the graphs, All of the schools used positive emotional words (e.g “true”, “good”, “money”, “present”, “mother”, “feeling”, “god” most frequently. The only exception is the school continental using “madness” the most frequently.

The order of schools in the wordcloud graphs below are as follows.: analytic, aristotle, capitalism, communism, continental, empiricism, feminism, german_idealism, nietzsche, phenomenology, plato, rationalism, stoicism

tidy_nrc_counts<- tidy_nrc %>% 
                  group_by(school) %>%
                  count(word) %>%
                  ungroup() 


for (school in unique(tidy_nrc_counts$school)) {

 df_school_nrc <- tidy_nrc_counts[which(tidy_nrc_counts$school == school),]

 wordcloud(df_school_nrc$word, df_school_nrc$n,
          main = school,
          scale = c(3,0.1),
          max.words = 100,
          min.freq = 1,
          random.order = FALSE,
          rot.per = 0.35,
          random.color = FALSE,
          colors=brewer.pal(12,"Paired"))
}

Clustering of emotions

From now on, we will see how many groups (clusters) we can group the philosophers into based on their emotional statuses using the K-means clustering algorithm. The first steps to create and train the model is to generate a training dataset : the training dataset is designed to have rows represent different authors and columns represent the number of times each emotional word was used.

1. training dataset

##           anger anticipation disgust  fear   joy negative positive sadness
## Aristotle 14885        25250   11728 21296 22245    37471    60772   17438
## Beauvoir   5994         9639    4884  7730 10377    14752    21109    6964
## Berkeley    637         1316     321   911   858     1844     3912     673
## Davis      2045         1772    1089  2374  1558     3435     4349    1845
## Deleuze    3857         6933    2679  6036  4124    11199    16680    5153
## Derrida    2187         3245    1148  3144  1967     4734     8402    2348
##           surprise trust
## Aristotle    11262 37059
## Beauvoir      3922 12604
## Berkeley       461  2051
## Davis          764  2590
## Deleuze       2801 11266
## Derrida       1293  4622

The second step is to implement data pre-processing : This step invovles row-normalizing each row in the dataset such all the rows have the same range of values across different columns. #2. data pre-processing

library(wordspace)
rows <- rownames(training_df)
cols <- colnames(training_df)
training_df_nu <- apply(training_df, 2, as.numeric)
training_df_normalized <- t(apply(training_df_nu, 1, function(x)(x - min(x)) / (max(x) - min(x))))

train_df <- as.data.frame(training_df_normalized)
rownames(train_df) <- authors

head(train_df)

##                anger anticipation    disgust      fear       joy  negative
## Aristotle 0.07317714    0.2825288 0.00941224 0.2026661 0.2218340 0.5293678
## Beauvoir  0.12055623    0.3326351 0.05597254 0.2215628 0.3755746 0.6301274
## Berkeley  0.08799777    0.2770816 0.00000000 0.1642996 0.1495405 0.4241158
## Davis     0.35732218    0.2811715 0.09065551 0.4490934 0.2214784 0.7450488
## Deleuze   0.08413685    0.3038354 0.00000000 0.2397686 0.1032069 0.6085280
## Derrida   0.14323132    0.2890819 0.00000000 0.2751585 0.1129032 0.4943479
##           positive    sadness    surprise     trust
## Aristotle        1 0.12474248 0.000000000 0.5210463
## Beauvoir         1 0.17699424 0.000000000 0.5051492
## Berkeley         1 0.09802283 0.038986355 0.4817600
## Davis            1 0.30153417 0.000000000 0.5093445
## Deleuze          1 0.17670166 0.008713663 0.6133133
## Derrida          1 0.16542597 0.019988972 0.4789082

The next step is to train a kmeans with different hyper-parameters ( the number of clusters ). Then the average silhouette score is plotted in the graphs below. #3. training (kmeans)

model <- kmeans(train_df, 3)

silhouette_score <- function(k){
  km <- kmeans(train_df, centers = k, nstart=25)
  ss <- silhouette(km$cluster, dist(train_df))
  mean(ss[, 3])
}

k <- 2:10

avg_sil <- sapply(k, silhouette_score)
plot(k, type='b', avg_sil, xlab='Number of clusters', ylab='Average Silhouette Scores', frame=FALSE)

fviz_nbclust(train_df, kmeans, method='silhouette')

As can been checked in the graphs above, the optimal number of cluster is 2 with the average silhouette score is around 0.4. In the following steps, we will see the distibution of philosophers in those two clusters.

Who’s in which cluster?

As in the graph above, “Nietzsche (continental)”, “Beauvoir (Existentialism)”, “Lenin (communism)”, ’Deleuze(continental)“,”Foucault(continental)“,”Davis(feminism)“,”Epictetus(stoicism)" are grouped into the same cluster (cluster 1) and the others are grouped into the same cluster ( cluster 2 ).

In the following step, the wordcloud graphs are created again to show which emotional words are frequently used by each of the two cluster groups.

In the graphs above, the cluster 1 and the cluster 2 exhibits relatively distinct frequency in their emotional words use. The cluster 2 has the word “death” and “madness” as the most frequently used emotional words, and it also uses some other non-positive emotional words such as “bad”, “evil”, “struggle”, etc fairly frequently. In contrast, the primarily used emotional words of cluster 1 are all positive words such as “good”, “god”, “true”, and “kind”.

In the following step, we can see the two clusters’ ratio of emotional words represented by each emotional category.

Above, as easily expected from the previous analysis, the cluster 1 shows higher “positive” words ratio in its emotional distribution whereas the cluster 2 shows higher ratio of “anger” and “disgust” words.

Thank you

Project1(ADS)

Sangmin Lee(sl4876)

9/17/2022